通过多曝光图像融合和细节增强实现单幅图像除雾

IF 3.7 Q1 PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH 安全科学与韧性(英文) Pub Date : 2023-12-22 DOI:10.1016/j.jnlssr.2023.11.003
Wenjing Mao , Dezhi Zheng , Minze Chen , Juqiang Chen
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引用次数: 0

摘要

户外摄像机在监控安全和社会治理方面发挥着重要作用。雾霾作为一种常见的天气现象,很容易影响摄像机的拍摄质量,造成图像细节的丢失和失真。本文基于人工多重曝光图像融合(AMEF)算法,提出了一种改进的多重曝光图像融合除雾技术。首先,对雾图像进行自适应曝光,然后通过多次曝光获得融合图像。融合权重由饱和度、对比度和亮度决定。最后,利用多尺度拉普拉斯算法对融合后的图像进行简单的自适应细节增强,以获得更清晰的除雾图像。经过主观和客观的验证,该算法可以在没有先验信息的情况下获得更多的图像细节和鲜明的图像色彩,从而有效提高除雾能力。
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Single image defogging via multi-exposure image fusion and detail enhancement

Outdoor cameras play an important role in monitoring security and social governance. As a common weather phenomenon, haze can easily affect the quality of camera shooting, resulting in loss and distortion of image details. This paper proposes an improved multi-exposure image fusion defogging technique based on the artificial multi-exposure image fusion (AMEF) algorithm. First, the foggy image is adaptively exposed, and the fused image is subsequently obtained via multiple exposures. The fusion weight is determined by the saturation, contrast, and brightness. Finally, the image fused by a multi-scale Laplacian algorithm is enhanced with simple adaptive details to obtain a clearer defogging image. It is subjectively and objectively verified that this algorithm can obtain more image details and distinct picture colors without a priori information, effectively improving the defogging ability.

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来源期刊
安全科学与韧性(英文)
安全科学与韧性(英文) Management Science and Operations Research, Safety, Risk, Reliability and Quality, Safety Research
CiteScore
8.70
自引率
0.00%
发文量
0
审稿时长
72 days
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